Understanding New Generations of Data – The Imperative for the Future-Focused Insurer

It is generally accepted that for insurance to match the majority of customer-facing industries in terms of customer service, omnichannel engagement and personalized products, insurers must undertake a series of radical organizational and technological transformations.

Of course, to effectively acquire customers, offer them personalized products and seamless service requires careful analysis of data from which insights can be drawn. However, the chief challenge to insurers’ effective use of analytics is data quality, or lack thereof (Insurance Nexus’ Advanced Analytics and AI survey).

This may, in part, be due to the evolving nature of data and our understanding of how its changing qualities impact how we use it. It stands to reason that the nature of data should alter over time; as technology changes and different data sources emerge, so the characteristics of data evolve. Despite this, while discussions about the proliferation of data are commonplace, considerations about the changing nature of data itself are less so.

Insurance Nexus spoke to three insurance data experts, Aviad Pinkovezky (Head of Product, Hippo Insurance), Jerry Gupta (Director of Group Strategy, Swiss Re Management (US) Corporation) and Eugene Wen (Vice President, Group Advanced Analytics, Manulife), for their perspectives on what each generation of data mean for insurance today, and how subsequent generations will influence insurance tomorrow.

Defining the different generations of data: from proprietary, third party, and IoT data, understanding how data evolves on a generational basis can help map strategy for future-focused insurers

The Next Generation: more data isn’t just more data. Understanding how its changing characteristics will impact the fundamental nature of insurance itself is crucial

Why ethics and caution are essential: trust is hard won and easily lost. A data breach, intentional or otherwise, can have catastrophic consequences

The conditions required to maximize data value: from acquiring the right talent, to data architecture and organizational structure, the right circumstances must be in place for effective data-integration